Dynamic failure analysis of process systems using neural networks

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dc.contributor.authorAdedigba, Sunday A.
dc.contributor.authorKhan, Faisal
dc.contributor.authorYang, Ming
dc.creatorSunday A., Adedigba
dc.date.accessioned2017-12-21T05:39:25Z
dc.date.available2017-12-21T05:39:25Z
dc.date.issued2017-10-01
dc.description.abstractAbstract Complex and non-linear relationships exist among process variables in a process operation. Owing to these complex and non-linear relationships potential accident modelling using an analytical technique is proving to be not very effective. The artificial neural network (ANN) is a powerful computational tool that assists in modelling complex and nonlinear relationships. This relationship has good potential to be generalized and used for subsequent failure analysis.This paper integrates ANNs with probabilistic analysis to model a process accident. A multi-layer perceptron (MLP) is used to define the relationship among process variables. The defined relationship is used to model a process accident considering logical and casual dependence of the variables. The predicted accident probability is subsequently used to estimate the likelihoods of failure to the process unit. A backward propagation technique is used to dynamically update the variable states and the failure probabilities accordingly.Integrating ANN with a probabilistic approach provides an efficient and effective way to estimate process accident probability as a function of time and thus the risk can be easily predicted upon quantifying the damage. The updating mechanism of the approach makes the model adaptive and captures evolving process conditions. The proposed integrated approach is applied to the Tennessee process system as a case study.en_US
dc.identifierDOI:10.1016/j.psep.2017.08.005
dc.identifier.citationSunday A. Adedigba, Faisal Khan, Ming Yang, Dynamic failure analysis of process systems using neural networks, In Process Safety and Environmental Protection, Volume 111, 2017, Pages 529-543en_US
dc.identifier.issn09575820
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S0957582017302483
dc.identifier.urihttp://nur.nu.edu.kz/handle/123456789/2996
dc.language.isoenen_US
dc.publisherProcess Safety and Environmental Protectionen_US
dc.relation.ispartofProcess Safety and Environmental Protection
dc.rights.license© 2017 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
dc.subjectArtificial neural network (ANN) analysisen_US
dc.subjectSequential accident modelen_US
dc.subjectAccident predictionen_US
dc.subjectReliability analysisen_US
dc.subjectSystem safetyen_US
dc.subjectRisk assessmenten_US
dc.titleDynamic failure analysis of process systems using neural networksen_US
dc.typeArticleen_US
elsevier.aggregationtypeJournal
elsevier.coverdate2017-10-01
elsevier.coverdisplaydateOctober 2017
elsevier.endingpage543
elsevier.identifier.doi10.1016/j.psep.2017.08.005
elsevier.identifier.eid1-s2.0-S0957582017302483
elsevier.identifier.piiS0957-5820(17)30248-3
elsevier.identifier.scopusid85028598731
elsevier.openaccess0
elsevier.openaccessarticlefalse
elsevier.openarchivearticlefalse
elsevier.startingpage529
elsevier.teaserComplex and non-linear relationships exist among process variables in a process operation. Owing to these complex and non-linear relationships potential accident modelling using an analytical technique...
elsevier.volume111
workflow.import.sourcescience

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